Performance evaluation of metaheuristic algorithms for femtosecond laser pulse optimization

Area of Science:

• Optics and Photonics
• Computational Physics
• Laser Science

Background:

• Ultrashort laser pulse shaping is crucial for advanced applications.
• Optimizing pulse characteristics often involves complex, high-dimensional parameter spaces.
• Traditional optimization methods can be time-consuming and may not find global optima.

Purpose of the Study:

• To investigate the efficacy of metaheuristic algorithms for ultrashort laser pulse optimization.
• To compare the performance of Grey Wolf Optimizer, Genetic Algorithm, and Crow Search Algorithm.
• To assess the robustness and universality of the proposed optimization approach.

Main Methods:

• Implementation and evaluation of three bio-inspired metaheuristic algorithms: Grey Wolf Optimizer, Genetic Algorithm, and Crow Search Algorithm.
• Optimization of laser pulses in a system including a mode-locked oscillator, spectral phase shaper, and nonlinear amplifier.
• Comparative analysis based on cost function convergence, autocorrelation traces, and optical spectra.

Main Results:

• Metaheuristic algorithms proved highly effective in minimizing ultrashort laser pulse duration.
• The Crow Search Algorithm consistently achieved the lowest cost function values and highest repeatability.
• Optimization results were validated through repeatability tests, demonstrating algorithm robustness.

Conclusions:

• Metaheuristic algorithms offer a powerful and efficient strategy for ultrashort laser pulse optimization.
• The Crow Search Algorithm shows particular promise for achieving high-quality pulse shaping.
• This intelligent optimization approach is broadly applicable to various laser systems in ultrafast photonics.